Spring 2026 Departmental Seminars & Lectures
During the Fall and Spring semesters, the Department of Biostatistics holds regular seminars on Thursdays, called the Levin Lecture Series, on a wide variety of topics which are of interest to both students and faculty. Over each semester, there are also often guest lectures outside the regular Thursday Levin Lecture Series, to provide a robust schedule the covers the wide range of topics in Biostatistics. The speakers are invited guests who spend the day of their seminar discussing their research with Biostatistics faculty and students. All Levin Lectures will be hosted over zoom, with the following credentials: Meeting ID:913 0905 0869; Passcode: 556019
Spring 2026 Schedule
Thursday, February 12th, Zoom
Levin Lecture
Liangyuan Hu, PhD
Associate Professor of Biostatistics and Epidemiology, Rutgers School of Public Health
Bayesian Machine Learning for Causal Inference and Real-World Evidence
Abstract:
In this talk, I will present a suite of Bayesian machine learning methods that strengthen causal inference with complex real-world data. First, I introduce riAFT-BART, a random-intercept accelerated failure time model that uses Bayesian additive regression trees to estimate causal effects of multiple treatments on clustered time-to-event outcomes. The approach flexibly captures nonlinear covariate effects and heterogeneous treatment responses in hierarchical data. I pair this model with a new Bayesian sensitivity analysis that quantifies how unmeasured confounding could alter posterior causal conclusions. Second, for longitudinal observational studies with time-to-event outcomes, I develop an alternative survival g-formula that embeds BART within the evolving generative components to reduce bias from model misspecification. Focusing on binary time-varying treatments, I propose a class of discrete-time survival g-formulas that incorporate longitudinal balancing scores for both static and dynamic treatment strategies, along with posterior sampling algorithms for inference. I also present a loss-based Bayesian sensitivity analysis that propagates uncertainty while assessing departures from the no unmeasured time-varying confounding assumption. I illustrate these methods in two applications: (i) using the National Cancer Database to compare three treatment strategies for high-risk localized prostate cancer with riAFT-BART and its sensitivity framework, and (ii) applying the new survival g-formula to electronic health record data from the Yale New Haven Health System.
Thursday, February 19th, Zoom
Levin Lecture
Xinyi Li, PhD
Assistant Professor, Mathematical and Statistical Sciences, Clemson University
Talk Title Forthcoming
Abstract:
Abstract Forthcoming
Thursday, February 26th, Zoom
Levin Lecture
Nancy R. Zhang, PhD
Ge Li and Ning Zhao Professor, Professor of Statistics and Data Science, The Wharton School at the University of Pennsylvania
Title Forthcoming
Abstract:
Abstract Forthcoming